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Karp–Flatt Metric
The Karp–Flatt metric is a measure of parallelization of code in parallel processor systems. This metric exists in addition to Amdahl's law and Gustafson's law as an indication of the extent to which a particular computer code is parallelized. It was proposed by Alan H. Karp and Horace P. Flatt in 1990. Description Given a parallel computation exhibiting speedup \psi on p processors, where p > 1, the experimentally determined serial fraction e is defined to be the Karp–Flatt Metric viz: :e = \frac The lower the value of e, the better the parallelization. Justification There are many ways to measure the performance of a parallel algorithm running on a parallel processor. The Karp–Flatt metric defines a metric which reveals aspects of the performance that are not easily discerned from other metrics. A pseudo-"derivation" of sorts follows from Amdahl's Law, which can be written as: :T(p) = T_s + \frac Where: *T(p) is the total time taken for code execution in a p-processor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parallelization
Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling.S.V. Adve ''et al.'' (November 2008)"Parallel Computing Research at Illinois: The UPCRC Agenda" (PDF). Parallel@Illinois, University of Illinois at Urbana-Champaign. "The main techniques for these performance benefits—increased clock frequency and smarter but increasingly complex architectures—are now hitting the so-called power wall. The computer industry has accepted that future performance increases must largely come from increasing the number of processors (or cores) on a die, rather than m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parallel Processor
Parallel computing is a type of computation in which many calculations or Process (computing), processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: Bit-level parallelism, bit-level, Instruction-level parallelism, instruction-level, Data parallelism, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling.S.V. Adve ''et al.'' (November 2008)"Parallel Computing Research at Illinois: The UPCRC Agenda" (PDF). Parallel@Illinois, University of Illinois at Urbana-Champaign. "The main techniques for these performance benefits—increased clock frequency and smarter but increasingly complex architectures—are now hitting the so-called power wall. The computer industry has accepted that future performance increases mus ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Amdahl's Law
In computer architecture, Amdahl's law (or Amdahl's argument) is a formula which gives the theoretical speedup in latency of the execution of a task at fixed workload that can be expected of a system whose resources are improved. It states that "the overall performance improvement gained by optimizing a single part of a system is limited by the fraction of time that the improved part is actually used". It is named after computer scientist Gene Amdahl, and was presented at the American Federation of Information Processing Societies (AFIPS) Spring Joint Computer Conference in 1967. Amdahl's law is often used in parallel computing to predict the theoretical speedup when using multiple processors. For example, if a program needs 20 hours to complete using a single thread, but a one-hour portion of the program cannot be parallelized, therefore only the remaining 19 hours' () execution time can be parallelized, then regardless of how many threads are devoted to a parallelized execution ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gustafson's Law
In computer architecture, Gustafson's law (or Gustafson–Barsis's law) gives the speedup in the execution time of a task that theoretically gains from parallel computing, using a hypothetical run of ''the task'' on a single-core machine as the baseline. To put it another way, it is the theoretical "slowdown" of an ''already parallelized'' task if running on a serial machine. It is named after computer scientist John L. Gustafson and his colleague Edwin H. Barsis, and was presented in the article ''Reevaluating Amdahl's Law'' in 1988. Definition Gustafson estimated the speedup S of a program gained by using parallel computing as follows: : \begin S &= s + p \times N \\ &= s + (1 - s) \times N \\ &= N + (1 - N) \times s \end where * S is the theoretical speedup of the program with parallelism (scaled speedup); *N is the number of processors; * s and p are the fractions of time spent executing the serial parts and the parallel parts of the program on the ''parallel'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Speedup
In computer architecture, speedup is a number that measures the relative performance of two systems processing the same problem. More technically, it is the improvement in speed of execution of a task executed on two similar architectures with different resources. The notion of speedup was established by Amdahl's law, which was particularly focused on parallel processing. However, speedup can be used more generally to show the effect on performance after any resource enhancement. Definitions Speedup can be defined for two different types of quantities: '' latency'' and ''throughput''. ''Latency'' of an architecture is the reciprocal of the execution speed of a task: : L = \frac = \frac, where * ''v'' is the execution speed of the task; * ''T'' is the execution time of the task; * ''W'' is the execution workload of the task. ''Throughput'' of an architecture is the execution rate of a task: : Q = \rho vA = \frac = \frac, where * ''ρ'' is the execution density (e.g., the number ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Serial Fraction
Serial may refer to: Arts, entertainment, and media The presentation of works in sequential segments * Serial (literature), serialised literature in print * Serial (publishing), periodical publications and newspapers * Serial (radio and television), series of radio and television programs that rely on a continuing plot * Serial film, a series of short subjects, with a continuing story, originally shown in theaters, in conjunction with feature films, particularly in the 1930s and 1940s * Indian serial, a type of Indian television program Other uses in arts, entertainment, and media * ''Serial'' (1980 film), based on McFadden's novel, starring Martin Mull and Tuesday Weld * ''Serial'' (podcast), a podcast spinoff of the radio series ''This American Life'' * ''The Serial: A Year in the Life of Marin County'', a 1977 novel by Cyra McFadden Computing and technology * SerDes, a Serializer/Deserializer (pronounced sir-deez) * Serial ATA * Serial attached SCSI * Serial bus, e.g., ** ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parallel Algorithm
In computer science, a parallel algorithm, as opposed to a traditional serial algorithm, is an algorithm which can do multiple operations in a given time. It has been a tradition of computer science to describe serial algorithms in abstract machine models, often the one known as random-access machine. Similarly, many computer science researchers have used a so-called parallel random-access machine (PRAM) as a parallel abstract machine (shared-memory). Many parallel algorithms are executed concurrently – though in general concurrent algorithms are a distinct concept – and thus these concepts are often conflated, with which aspect of an algorithm is parallel and which is concurrent not being clearly distinguished. Further, non-parallel, non-concurrent algorithms are often referred to as "sequential algorithms", by contrast with concurrent algorithms. Parallelizability Algorithms vary significantly in how parallelizable they are, ranging from easily parallelizable to completely ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Amdahl's Law
In computer architecture, Amdahl's law (or Amdahl's argument) is a formula which gives the theoretical speedup in latency of the execution of a task at fixed workload that can be expected of a system whose resources are improved. It states that "the overall performance improvement gained by optimizing a single part of a system is limited by the fraction of time that the improved part is actually used". It is named after computer scientist Gene Amdahl, and was presented at the American Federation of Information Processing Societies (AFIPS) Spring Joint Computer Conference in 1967. Amdahl's law is often used in parallel computing to predict the theoretical speedup when using multiple processors. For example, if a program needs 20 hours to complete using a single thread, but a one-hour portion of the program cannot be parallelized, therefore only the remaining 19 hours' () execution time can be parallelized, then regardless of how many threads are devoted to a parallelized execution ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Descriptive Statistic
A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information, while descriptive statistics (in the mass noun sense) is the process of using and analysing those statistics. Descriptive statistic is distinguished from inferential statistics (or inductive statistics) by its aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. This generally means that descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory, and are frequently nonparametric statistics. Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented. For example, in papers reporting on human subjects, typically a table is included giving the overall sample size, sample sizes in important subgroups (e.g., for each treatment or ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Science
Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to Applied science, practical disciplines (including the design and implementation of Computer architecture, hardware and Computer programming, software). Computer science is generally considered an area of research, academic research and distinct from computer programming. Algorithms and data structures are central to computer science. The theory of computation concerns abstract models of computation and general classes of computational problem, problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and for preventing Vulnerability (computing), security vulnerabilities. Computer graphics (computer science), Computer graphics and computational geometry address the generation of images. Progr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithmic Efficiency
In computer science, algorithmic efficiency is a property of an algorithm which relates to the amount of computational resources used by the algorithm. An algorithm must be analyzed to determine its resource usage, and the efficiency of an algorithm can be measured based on the usage of different resources. Algorithmic efficiency can be thought of as analogous to engineering productivity for a repeating or continuous process. For maximum efficiency it is desirable to minimize resource usage. However, different resources such as time and space complexity cannot be compared directly, so which of two algorithms is considered to be more efficient often depends on which measure of efficiency is considered most important. For example, bubble sort and timsort are both algorithms to sort a list of items from smallest to largest. Bubble sort sorts the list in time proportional to the number of elements squared (O(n^2), see Big O notation), but only requires a small amount of extra mem ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Load Balancing (computing)
In computing, load balancing is the process of distributing a set of tasks over a set of resources (computing units), with the aim of making their overall processing more efficient. Load balancing can optimize the response time and avoid unevenly overloading some compute nodes while other compute nodes are left idle. Load balancing is the subject of research in the field of parallel computers. Two main approaches exist: static algorithms, which do not take into account the state of the different machines, and dynamic algorithms, which are usually more general and more efficient but require exchanges of information between the different computing units, at the risk of a loss of efficiency. Problem overview A load-balancing algorithm always tries to answer a specific problem. Among other things, the nature of the tasks, the algorithmic complexity, the hardware architecture on which the algorithms will run as well as required error tolerance, must be taken into account. Therefore c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
